A Plastic Contamination Image Dataset for Deep Learning Model Development and Training
The removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at t...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/2/2/21 |
| _version_ | 1797567317517795328 |
|---|---|
| author | Mathew G. Pelletier Greg A. Holt John D. Wanjura |
| author_facet | Mathew G. Pelletier Greg A. Holt John D. Wanjura |
| author_sort | Mathew G. Pelletier |
| collection | DOAJ |
| description | The removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at the gin, automatic inspection systems are needed to detect and control removal systems. Due to significant cost constraints in the U.S. cotton ginning industry, the use of low-cost color cameras for detection of plastic contamination has been successfully adopted. However, some plastics of similar color to background are difficult to detect when utilizing traditional machine learning algorithms. Hence, current detection/removal system designs are not able to remove all plastics and there is still a need for better detection methods. Recent advances in deep learning convolutional neural networks (CNNs) show promise for enabling the use of low-cost color cameras for detection of objects of interest when placed against a background of similar color. They do this by mimicking the human visual detection system, focusing on differences in texture rather than color as the primary detection paradigm. The key to leveraging the CNNs is the development of extensive image datasets required for training. One of the impediments to this methodology is the need for large image datasets where each image must be annotated with bounding boxes that surround each object of interest. As this requirement is labor-intensive, there is significant value in these image datasets. This report details the included image dataset as well as the system design used to collect the images. For acquisition of the image dataset, a prototype detection system was developed and deployed into a commercial cotton gin where images were collected for the duration of the 2018–2019 ginning season. A discussion of the observational impact that the system had on reduction of plastic contamination at the commercial gin, utilizing traditional color-based machine learning algorithms, is also included. |
| first_indexed | 2024-03-10T19:40:06Z |
| format | Article |
| id | doaj.art-cdef9175de0b4b8bbe8aba2a754b5b6e |
| institution | Directory Open Access Journal |
| issn | 2624-7402 |
| language | English |
| last_indexed | 2024-03-10T19:40:06Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj.art-cdef9175de0b4b8bbe8aba2a754b5b6e2023-11-20T01:21:10ZengMDPI AGAgriEngineering2624-74022020-05-012231732110.3390/agriengineering2020021A Plastic Contamination Image Dataset for Deep Learning Model Development and TrainingMathew G. Pelletier0Greg A. Holt1John D. Wanjura2Agricultural Research Services, United States Department of Agriculture, Lubbock, TX 79404, USAAgricultural Research Services, United States Department of Agriculture, Lubbock, TX 79404, USAAgricultural Research Services, United States Department of Agriculture, Lubbock, TX 79404, USAThe removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at the gin, automatic inspection systems are needed to detect and control removal systems. Due to significant cost constraints in the U.S. cotton ginning industry, the use of low-cost color cameras for detection of plastic contamination has been successfully adopted. However, some plastics of similar color to background are difficult to detect when utilizing traditional machine learning algorithms. Hence, current detection/removal system designs are not able to remove all plastics and there is still a need for better detection methods. Recent advances in deep learning convolutional neural networks (CNNs) show promise for enabling the use of low-cost color cameras for detection of objects of interest when placed against a background of similar color. They do this by mimicking the human visual detection system, focusing on differences in texture rather than color as the primary detection paradigm. The key to leveraging the CNNs is the development of extensive image datasets required for training. One of the impediments to this methodology is the need for large image datasets where each image must be annotated with bounding boxes that surround each object of interest. As this requirement is labor-intensive, there is significant value in these image datasets. This report details the included image dataset as well as the system design used to collect the images. For acquisition of the image dataset, a prototype detection system was developed and deployed into a commercial cotton gin where images were collected for the duration of the 2018–2019 ginning season. A discussion of the observational impact that the system had on reduction of plastic contamination at the commercial gin, utilizing traditional color-based machine learning algorithms, is also included.https://www.mdpi.com/2624-7402/2/2/21machine visionplastic contaminationcottonautomated inspection |
| spellingShingle | Mathew G. Pelletier Greg A. Holt John D. Wanjura A Plastic Contamination Image Dataset for Deep Learning Model Development and Training AgriEngineering machine vision plastic contamination cotton automated inspection |
| title | A Plastic Contamination Image Dataset for Deep Learning Model Development and Training |
| title_full | A Plastic Contamination Image Dataset for Deep Learning Model Development and Training |
| title_fullStr | A Plastic Contamination Image Dataset for Deep Learning Model Development and Training |
| title_full_unstemmed | A Plastic Contamination Image Dataset for Deep Learning Model Development and Training |
| title_short | A Plastic Contamination Image Dataset for Deep Learning Model Development and Training |
| title_sort | plastic contamination image dataset for deep learning model development and training |
| topic | machine vision plastic contamination cotton automated inspection |
| url | https://www.mdpi.com/2624-7402/2/2/21 |
| work_keys_str_mv | AT mathewgpelletier aplasticcontaminationimagedatasetfordeeplearningmodeldevelopmentandtraining AT gregaholt aplasticcontaminationimagedatasetfordeeplearningmodeldevelopmentandtraining AT johndwanjura aplasticcontaminationimagedatasetfordeeplearningmodeldevelopmentandtraining AT mathewgpelletier plasticcontaminationimagedatasetfordeeplearningmodeldevelopmentandtraining AT gregaholt plasticcontaminationimagedatasetfordeeplearningmodeldevelopmentandtraining AT johndwanjura plasticcontaminationimagedatasetfordeeplearningmodeldevelopmentandtraining |